Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagation on a user-item bipartite graph, and then provide the users with personalized item suggestions based on the representations. Despite effectiveness, existing algorithms neglect precious interactive features between user-item pairs in the embedding process. When predicting a user's preference for different items, they still aggregate the user tree in the same way, without emphasizing target-related information in the user neighborhood. Such a uniform aggregation scheme easily leads to suboptimal user and item representations, limiting the model expressiveness to some extent. In this work, we address this problem by building bilateral interactive guidance between each user-item pair and proposing a new model named IA-GCN (short for InterActive GCN). Specifically, when learning the user representation from its neighborhood, we assign higher attention weights to those neighbors similar to the target item. Correspondingly, when learning the item representation, we pay more attention to those neighbors resembling the target user. This leads to interactive and interpretable features, effectively distilling target-specific information through each graph convolutional operation. Our model is built on top of LightGCN, a state-of-the-art GCN model for CF, and can be combined with various GCN-based CF architectures in an end-to-end fashion. Extensive experiments on three benchmark datasets demonstrate the effectiveness and robustness of IA-GCN.

翻译：近期，图卷积网络已成为协同过滤推荐系统领域的最新代表性技术。通过在用户-物品二分图上执行嵌入传播来学习用户和物品的有效表示，进而基于这些表示为用户提供个性化物品推荐，已成为常见做法。尽管现有算法具有一定效果，但在嵌入过程中忽略了用户-物品对之间的交互特征。当预测用户对不同物品的偏好时，现有方法仍以统一方式聚合用户邻域，未能强调与目标项相关的用户邻域信息。这种统一聚合策略易导致用户和物品表示次优，在一定程度上限制了模型表达能力。针对这一问题，本文通过在每一对用户-物品间构建双向交互引导，提出名为IA-GCN（交互式图卷积网络）的新模型。具体而言，在学习用户邻域的表示时，对与目标物品相似度较高的邻居赋予更高注意力权重；相应地，在学习物品表示时，更多关注与目标用户相似的邻居。这产生了可交互且可解释的特征，通过每次图卷积操作有效提取目标特定信息。本模型基于当前CF领域最先进的图卷积网络模型LightGCN构建，且能以端到端方式与多种基于图卷积网络的CF架构相结合。在三个基准数据集上的大量实验验证了IA-GCN的有效性与鲁棒性。